Summary of Autopv: Automatically Design Your Photovoltaic Power Forecasting Model, by Dayin Chen et al.
AutoPV: Automatically Design Your Photovoltaic Power Forecasting Model
by Dayin Chen, Xiaodan Shi, Mingkun Jiang, Haoran Zhang, Dongxiao Zhang, Yuntian Chen, Jinyue Yan
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces AutoPV, a novel framework for automated photovoltaic power forecasting (PVPF) model construction using neural architecture search (NAS) technology. The framework addresses the challenge of constructing optimal predictive architectures for specific PVPF tasks by developing a brand new NAS search space that incorporates data processing techniques from state-of-the-art time series forecasting (TSF) models and typical PVPF deep learning models. Experimental results demonstrate that AutoPV can construct effective PVPF models in a relatively short time, outperforming predefined models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AutoPV is a new way to make better predictions about solar energy production. Scientists have been working on this problem for a long time, but it’s hard to find the best approach without a lot of expertise and effort. This paper shows how to use special technology called neural architecture search (NAS) to automatically build good models for predicting solar energy production. The results are exciting – the new method can quickly create better models than the ones scientists currently use. |
Keywords
» Artificial intelligence » Deep learning » Time series
